- Multiclass classification sorts data into more than two classes; binary classification sorts it into two.
- Common multiclass classifiers include naive Bayes, neural networks, k-nearest neighbors (kNN) and decision trees.
- kNN is non-parametric: it memorizes training data rather than learning a discriminative function.
- Decision trees are easy to interpret but can change entirely with small variations in the data.
- Examples include face classification, animal species classification and optical character recognition.
Classifiers Used in Multiclass Classification
Various algorithms are used in multiclass classification such as naive bayes, neural networks, k-nearest neighbors (kNN), and decision trees.
Naive Bayes
Naive Bayes are parametric algorithms that require a set of assumptions to improve the learning process of the machine while using parameters independent of the training data size.
Advantages
- Easy to build and efficient for training models and making predictions
- No hyperparameter tuning can give better results
- Excellent text classifier
Disadvantage
- It is a linear classifier, which is not suitable for classes that are not linearly separate
K-nearest neighbor
K-nearest neighbor (kNN), a simple and powerful supervised machine learning (ML) algorithm, is used to solve regression and classification problems. It memorizes training data instead of learning a discriminative function from the data.
Advantages
- kNN is a non-parametric classifier, and does not make assumptions about the distribution of classes.
- Can be used universally in multiclass classification
- No training step beyond storing the data
- It is easy to implement
Disadvantages
- K value must work with the testing and training data, making it difficult to find.
- It memorizes instead of learning.
- It measures distance with each data point, making it exhaustive.
Decision Trees
Like flow charts, decision trees are structures which use conditional statements to guide a decision path. Because they are easy to interpret, decision trees are one of the most frequently used supervised learning methods. Additionally, they map both linear and non-linear relationships.
Advantages
- Decision trees are ideal for classification.
- Can compute binary or multiclass classification problems.
Disadvantages
- Can create overly-complex trees.
- Small variations in the data can result in an entirely different tree being created.
- Create biased trees if one or more classes dominate.
Binary vs. Multiclass Classification
The algorithms discussed above can be used in both binary and multiclass classification. However, there are differences in these classifications and how they are used.
Binary classification is used to organize data into two classes. Examples of binary classification include: email spam detection, churn prediction, and conversion prediction.
Multiclass classification permits multiple classes. Examples of multiclass classification include: face classification, animal species classification, and optical character recognition.
How is Multiclass Classification Used?
Multiclass classification has a variety of applications. It can be used to identify animals from images and sort them into categories. Cybersecurity companies can use multiclass classification to sort incoming emails into categories such as spam, phishing or safe. It can also be used when analyzing an individual’s mood into more than positive or negative. Instead, it will use multiple categories such as, happy, sad, depressed, excited, etc. There is no limit to the amount of classes used in multiclass classification.
Multiclass Classification in H2O AI
H2O Driverless AI and open-source H2O-3 both handle multiclass classification, alongside binary classification and regression.